Exploring Domain-Wall Pinning in Ferroelectrics via Automated High-Throughput Atomic Force Microscopy

Area of Science:

• Condensed Matter Physics
• Materials Science
• Nanotechnology

Background:

• Domain-wall dynamics in ferroelectric materials are highly sensitive to local microstructure.
• Spatially resolved studies are crucial for understanding wall pinning, but traditional methods are time-consuming.
• Sparse pinning centers and domain walls limit dense imaging techniques.

Purpose of the Study:

• To quantify electric-field-driven domain-wall dynamics in ferroelectric-ferroelastic structures.
• To investigate the influence of local microstructure on domain-wall displacement.
• To develop a predictive framework for designing ferroelectric memories based on microstructure-specific rules.

Main Methods:

• Utilized machine learning-controlled automated piezoresponse force microscopy (AFM) on a large-area epitaxial PbTiO3 film.
• Analyzed 1500 domain switching events to correlate domain-wall displacement with field parameters and local configurations.
• Characterized ferroelectric and ferroelastic wall orientations and their response to varying bias.

Main Results:

• Domain-wall displacement is dependent on both applied electric field parameters and the local ferroelectric-ferroelastic configuration.
• Different domain boundary types exhibit distinct pinning behaviors and activation fields (e.g., twin boundaries vs. single-variant boundaries).
• Automated AFM workflow enabled high-throughput statistics crucial for predictive modeling.

Conclusions:

• A microstructure-specific rule set linking domain configurations to pulse parameters was established.
• This understanding forms the foundation for designing advanced ferroelectric memories.
• The developed automated methodology accelerates the characterization of domain-wall dynamics.